Glucose Control System Research Articles

Critical Care Management of Stress-Induced Hyperglycemia.

We discuss key studies that have set the scene for the debate on the efficacy and safety of tight glycemic control in critically ill patients, highlighting important differences among them, and describe the ensuing search towards strategies for safer glucose control. Differences in level of glycemic control, glucose measurement and insulin administration, expertise, and nutritional management may explain the divergent outcomes of the landmark studies on tight glycemic control in critical illness. Regarding strategies towards safer glucose control, several computerized algorithms have shown promise, but lack validation in adequately powered outcome studies. Real-time continuous glucose monitoring and closed loop blood glucose control systems are not up to the task yet due to technical challenges, though recent advances are promising. Alternatives for insulin have only been investigated in small feasibility studies. Severe hyperglycemia in critically ill patients generally is not tolerated anymore, but the optimal blood glucose target may depend on the specific patient and logistic context.

Glucose-insulin model identified in free-living conditions for hypoglycaemia prevention

Abstract Hypoglycaemia avoidance is one of the main barriers to the optimal management of Type 1 Diabetes (T1D). In order to attenuate the effect of hypoglycaemia, alarm systems support T1D subjects equipped with Continuous Glucose Monitoring (CGM) devices. The development of predictive detection tools for hypoglycaemia and CGM have been accelerated by Artificial Pancreas (AP), a closed-loop systems for automatic blood glucose control in T1D subjects. The methods to generate hypoglycaemia alarms can be divided in two categories: low-threshold detection and prediction. The first notifies the crossing of a critical blood glucose level, while the second predicts this risk in advance and it is typically based on a patient model. Considering the significant inter-patient variability characterizing T1D subjects, patient-tailored models are required. In this regard, different individualization techniques have been proposed showing significant improvements compared to “average” models. This paper proposes an alarm system based on patient-tailored models obtained through an identification technique that exploits impulse response data collected in silico and that is extended here to be used on free-living data. In particular, the data used in this work derive from a 1 month AP trial performed in free-living conditions. Individualized models obtained with different identification parameters are compared. Independently of the selected parameters, the patient-tailored models show superior predictive performance with respect to the “average” model used in the Model Predictive Control (MPC) algorithm used in the trial. The best model is used to design an alarm system which shows significant improvements in hypoglycaemia detection in comparison with the safety system used in the trial: true positive are increased by 31% with a decrease of the false positive by 57%. The promising prediction capabilities of the proposed patient-tailored models can be a key ingredient for a new generation of individualised MPC for AP.

Insulin Estimation and Prediction: A Review of the Estimation and Prediction of Subcutaneous Insulin Pharmacokinetics in Closed-Loop Glucose Control

Patients affected by type 1 diabetes (T1D) suffer from lack of insulin secretion by the pancreas, due to the autoimmune destruction of the insulin-producing beta cells. This translates into elevated plasma glucose concentration with deleterious effects in the cardiovascular system, leading to serious long-term complications. With the improvement of continuous glucose monitoring (CGM) technology, a significant amount of research has been focused in the last decade on the development of an artificial pancreas (AP), that is, a closed-loop glucose control system that automatically dispenses insulin. The AP is now closer to reality [1]. An AP system was first approved by the U.S. Food and Drug Administration (FDA) in 2016 [2]. The system still requires user intervention at mealtime, as currently done in openloop insulin pump therapy, to administer an insulin bolus based on carbohydrate counting (a hybrid closed-loop system).

Fault detection in glucose control: Is it time to move beyond CGM data?

Abstract People with diabetes mellitus type 1 could benefit from fully automated systems for glucose control. However, faults in any component of the system can severely compromise the safety of the user. An increasing degree of automation also increases the risk that faults remain undiscovered for longer periods - unless automated routines for fault detection are implemented at the same time. The aim of this article is to give a categorized overview of methods for fault detection in glucose control systems. This overview targets at disclosing hidden potentials for improvement and unresolved issues. Methods for fault detection in glucose control systems have been reviewed and classified with respect to categories such as the type of method and the exploited data basis. Both journal and conference papers were taken into account. Compared to the number of studies on glucose control algorithms, only a few articles have been published on fault detection. Surprisingly few of them consider system information beyond the standard diabetes care data.

Comparison of Space Glucose Control and Routine Glucose Management Protocol for Glycemic Control in Critically Ill Patients: A Prospective, Randomized Clinical Study.

Background:The Space Glucose Control (SGC) system is a computer-assisted device combining infusion pumps with the enhanced Model Predictive Control algorithm to achieve the target blood glucose (BG) level safely. The objective of this study was to evaluate the efficacy and safety of glycemic control by SGC with customized BG target range of 5.8–8.9 mmol/L in the critically ill patients.Methods:It is a randomized controlled trial of seventy critically ill patients with mechanical ventilation and hyperglycemia (BG ≥ 9.0 mmol/L). Thirty-six patients in the SGC group and 34 in the routine glucose management group were observed for three consecutive days. Target BG for both groups was 5.8–8.9 mmol/L. The primary outcome was the percentage time in the target range.Results:The percentage time within BG target range in the SGC group (69 ± 15%) was significantly higher than in the routine management group (52 ± 24%; P < 0.01). No measurement was ≤2.2 mmol/L, and there was only one episode of hypoglycemia (2.3–3.3 mmol/L) in each group. The average BG was significantly lower in the SGC group (7.8 ± 0.7 mmol/L) than in the routine management group (9.1 ± 1.6 mmol/L, P < 0.001). Target BG level was reached earlier in the SGC group than routine management group (2.5 ± 2.9 vs. 12.1 ± 15.3 h, P = 0.001). However, the SGC group performed worse for daily insulin requirement (59.8 ± 39.3 vs. 28.4 ± 36.7 U, P = 0.001) and sampling interval (2.0 ± 0.5 vs. 3.7 ± 0.5 h, P < 0.001) than the routine management group did. Multiple linear regression showed that the intervention group remained a significant individual predictor (P < 0.001) of the percentage time in target range.Conclusions:The SGC system, with a BG target of 5.8–8.9 mmol/L, resulted in effective and reliable glycemic control with few hypoglycemic episodes in critically ill patients with mechanical ventilation and hyperglycemia. However, the workload was increased.Trial Registration:http://www.clinicaltrials.gov, NCT 02491346; https://www.clinicaltrials.gov/ct2/show/NCT02491346?term=NCT02491346&cond=Hyperglycemia&cntry1=ES%3ACN&rank=1.

Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator – in silico evaluation under intra-day variability

Current prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain. In this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake. Overall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4vs. 131.8 ± 4.2mg/dl; percentage time in target [70, 180]mg/dl, 82.0 ± 7.0vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4vs. 140.5 ± 13.0mg/dl; percentage time in target, 65.9 ± 12.9vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed. Using an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.

Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings. A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses. Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = -.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices. These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals.

Single- and Dual-Hormone Artificial Pancreas for Overnight Glucose Control in Type 1 Diabetes.

The added benefit of glucagon in artificial pancreas systems for overnight glucose control in type 1 diabetes has not been fully explored. The objective of the study was to compare the efficacy of dual-hormone (insulin and glucagon) artificial pancreas, single-hormone (insulin alone) artificial pancreas, and conventional insulin pump therapy. This study was a three-center, three-arm, open-label, randomized, crossover controlled trial involving three interventions, each applied over a night after a high carbohydrate/high fat meal and a second after exercise to mimic real-life glycemic excursions. The study was conducted in a home setting. Twenty-eight type 1 diabetes participants (21 adults and seven adolescents) participated in the study. Dual-hormone artificial pancreas, single-hormone artificial pancreas, and conventional pump therapy was activated from 9:00 PM to 7:00 AM. The main outcome was a proportion of time in target (4-8 mmol/L) by continuous glucose monitoring from 11:00 PM to 7:00 AM. Analysis was by intention to treat. The median (interquartile range) percentage of time-in-target glucose range was 47% (36%-71%) for conventional therapy, higher on both single-hormone (76% [65%-91%], P < .001) and dual-hormone artificial pancreas (81 [68%-93%], P < .001). The median (interquartile range) time spent below 4 mmol/L was 14% (4%-28%) for conventional therapy, lower on both single-hormone (5% [0%-13%], P = .004) and dual-hormone artificial pancreas (1% [0%-8%], P < .001). There were 14 hypoglycemic events on conventional therapy compared with six incidences on the single-hormone artificial pancreas (P = .059) and three incidences on the dual-hormone artificial pancreas (P = .017). None of these outcomes differed significantly between single- and dual-hormone configurations. Single- and dual-hormone artificial pancreas systems both provided better glucose control than conventional therapy. Although the dual-hormone configuration did not increase overnight time-in-target glucose levels, an effect on lowering hypoglycemia risk cannot be ruled out.

Real-time estimation of plasma insulin concentration from continuous glucose monitor measurements

Continuous glucose monitors can measure interstitial glucose concentration in real time for closed-loop glucose control systems, known as artificial pancreas. These control systems use an insulin feedback to maintain plasma glucose concentration within a narrow and safe range, and thus to avoid health complications. As it is not possible to measure plasma insulin concentration in real time, insulin models have been used in literature to estimate them. Nevertheless, the significant inter- and intra-patient variability of insulin absorption jeopardizes the accuracy of these estimations. In order to reduce these limitations, our objective is to perform a real-time estimation of plasma insulin concentration from continuous glucose monitoring (CGM). Hovorka’s glucose–insulin model has been incorporated in an extended Kalman filter in which different selected time-variant model parameters have been considered as extended states. The observability of the original Hovorka’s model and of several extended models has been evaluated by their Lie derivatives. We have evaluated this methodology with an in-silico study with 100 patients with Type 1 diabetes during 25 h. Furthermore, it has been also validated using clinical data from 12 insulin pump patients with Type 1 diabetes who underwent four mixed meal studies. Real-time insulin estimations have been compared to plasma insulin measurements to assess performance showing the validity of the methodology here used in comparison with that formerly used for insulin models. Hence, real-time estimations for plasma insulin concentration based on subcutaneous glucose monitoring can be beneficial for increasing the efficiency of control algorithms for the artificial pancreas.

Outpatient overnight glucose control with dual-hormone artificial pancreas, single-hormone artificial pancreas, or conventional insulin pump therapy in children and adolescents with type 1 diabetes: an open-label, randomised controlled trial

Additional benefits of the dual-hormone (insulin and glucagon) artificial pancreas compared with the single-hormone (insulin alone) artificial pancreas have not been assessed in young people in outpatient unrestricted conditions. We evaluated the efficacy of three systems for nocturnal glucose control in children and adolescents with type 1 diabetes. We did a randomised, three-way, crossover trial in children aged 9-17 years with type 1 diabetes attending a diabetes camp in Canada. With use of sealed envelopes, children were randomly assigned in a 1:1:1:1:1:1 ratio with blocks of six to different sequences of the three interventions (single-hormone artificial pancreas, dual-hormone artificial pancreas, and conventional continuous subcutaneous insulin pump therapy). Each intervention was applied for 3 consecutive nights. Participants, study staff, and endpoint assessors were not masked. The primary outcome was the percentage of time spent with glucose concentrations lower than 4·0 mmol/L from 2300 h to 0700 h. Analysis was by intention to treat. A p value of less than 0·0167 was regarded as significant. This study is registered with ClinicalTrials.gov, number NCT02189694. Between June 30, 2014, and Aug 9, 2014, we enrolled 33 children of mean age 13·3 years (SD 2·3; range 9-17). The time spent at a glucose concentration lower than 4·0 mmol/L was median 0% (IQR 0·0-2·4) during nights with the dual-hormone artificial pancreas, 3·1% (0·0-6·9) during nights with the single-hormone artificial pancreas (p=0·032), and 3·4% (0-11·0) during nights with conventional pump therapy (p=0·0048 compared with dual-hormone artificial pancreas and p=0·32 compared with single-hormone artificial pancreas). 15 hypoglycaemic events (<3·1 mmol/L for 20 min measured by sensor then confirmed with capillary glucose <4·0 mmol/L) were noted during nights with conventional pump therapy compared with four events with the single-hormone system and no events with the dual-hormone system. None of the assessed outcomes varied with the order in which children and young adults were assigned interventions. The dual-hormone artificial pancreas could improve nocturnal glucose control in children and adolescents with type 1 diabetes. Longer and larger outpatient studies are now needed. Canadian Diabetes Association, Fondation J A De Sève.

Closed-loop control for pediatric Type 1 diabetes mellitus

SUMMARY Closed-loop clinical trials have resulted in significant advances with continuous glucose monitoring and control systems modulating insulin delivery. Those trials were performed in closely supervised clinical research settings; while adults with Type 1 diabetes were initially targeted, studies in children with Type 1 diabetes have followed in both clinical research units and pediatric diabetes camps. These studies have been conducted as multicenter and multinational efforts. Pediatric studies have since been piloted in home settings overnight for control during sleep. The stage is now set for accelerating efforts, extending the number of patients enrolled, the amount of time during which the system is active daily and the duration of the clinical trials.

Recent Trends in Blood Glucose Control Studies

Diabetes mellitus is a disease of the endocrine system in which blood glucose levels are constantly above the normal range. Since insulin-dependent diabetic patients require insulin administration to maintain blood glucose levels within the normal range, automatic blood glucose control methods to reduce the burden of such open-loop insulin therapy have been studied. In this review, we first introduce some important mathematical models of glucoseinsulin metabolism, followed by some representative blood glucose control systems, where most of them use model predictive control as control algorithm, developed to date and discuss their implications in the near future.

Postprandial blood glucose control in type 1 diabetes for carbohydrates with varying glycemic index foods.

Treatment of type 1 diabetes consists of maintaining postprandial normoglycemia using the correct prandial insulin dose according to food intake. Nonetheless, it is hardly achieved in practice, which results in several diabetes-related complications. In this study we present a feedforward plus feedback blood glucose control system that considers the glycemic index of foods. It consists of a preprandial insulin bolus whose optimal bolus dose and timing are stated as a minimization problem, which is followed by a postprandial closed-loop control based on model predictive control. Simulation results show that, for a representative carbohydrate intake of 50 g, the present control system is able to maintain postprandial glycemia below 140 mg/dL while preventing postprandial hypoglycemia as well.

Glucose control in intensive care: usability, efficacy and safety of Space GlucoseControl in two medical European intensive care units.

BackgroundThe Space GlucoseControl system (SGC) is a nurse-driven, computer-assisted device for glycemic control combining infusion pumps with the enhanced Model Predictive Control algorithm (B. Braun, Melsungen, Germany). We aimed to investigate the performance of the SGC in medical critically ill patients.MethodsTwo open clinical investigations in tertiary centers in Graz, Austria and Zurich, Switzerland were performed. Efficacy was assessed by percentage of time within the target range (4.4-8.3 mmol/L; primary end point), mean blood glucose, and sampling interval. Safety was assessed by the number of hypoglycemic episodes (≤2.2 mmol/L) and the percentage of time spent below this cutoff level. Usability was analyzed with a standardized questionnaire given to involved nursing staff after the trial.ResultsForty medical critically ill patients (age, 62 ± 15 years; body mass index, 30.0 ± 8.9 kg/m2; APACHE II score, 24.8 ± 5.4; 27 males; 8 with diabetes) were included for a period of 6.5 ± 3.7 days (n = 20 in each center). The primary endpoint (time in target range 4.4 to 8.3 mmol/l) was reached in 88.3% ± 9.3 of the time and mean arterial blood glucose was 6.7 ± 0.4 mmol/l. The sampling interval was 2.2 ± 0.4 hours. The mean daily insulin dose was 87.2 ± 64.6 IU. The adherence to the given insulin dose advice was high (98.2%). While the percentage of time spent in a moderately hypoglycemic range (2.2 to 3.3 mmol/L) was low (0.07 ± 0.26% of the time), one severe hypoglycemic episode (<2.2 mmol/L) occurred (2.5% of patients or 0.03% of glucose readings).ConclusionsSGC is a safe and efficient method to control blood glucose in critically ill patients as assessed in two European medical intensive care units.

Wearable systems and mobile applications for diabetes disease management

Diabetes is a chronic metabolic disorder requiring the active and continuous participation of diabetic individuals, their caregivers and physicians in order to be managed and well-controlled. Diabetes care has evolved considerably over the years with the development of advanced medical technologies and therapeutics. In the first part of this paper, we present the state of the art in wearable medical devices for monitoring and controlling blood glucose levels with special emphasis on continuous glucose monitoring (CGM) devices and insulin pumps. Moreover, a description is made of the significant steps taken towards a portable fully automated closed-loop glucose control system (artificial pancreas) suitable for outpatient use. The second part of this paper is concerned with mobile self-management support interventions in diabetes care. Advances in mobile computing and wireless communications have enabled the development of efficient mobile applications for self-monitoring diabetes and providing feedback. Both literature and digital distribution stores demonstrate a plethora of such paradigms with, admittedly, obvious variety in their functionalities, design and validity. The way in which mobile health technology has contributed to improvements in diabetes health outcomes and life is also discussed and future directions for research are identified.

Are closed-loop systems for intensive insulin therapy ready for prime time in the ICU?

Recent findings suggest that the effects of tight glycemic control (TGC) performing intensive insulin therapy (IIT) in medical and surgical ICU have had conflicting results. The purpose of this review is to summarize the current evidence in humans how closed-loop systems for IIT are ready for prime time in the ICU. Current evidence suggests that maintaining normoglycemia postoperatively can improve the outcome and reduce the mortality and morbidity of critically ill patients by TGC performing IIT according to the large randomized trials. However, trials examining the effects of TGC have had conflicting results. Systematic reviews and meta-analyses have also led to differing conclusions. The main reason these clinical trials and meta-analyses were negative results for TGC was because of the high incidence of hypoglycemia. This could not be prevented as there is no reliable technique currently able to avoid this condition during IIT. The development of accurate, continuous blood glucose monitoring devices, and closed-loop systems for computer-assisted blood glucose control in the ICU, will probably help avoid hypoglycemia in these situations. The challenge in the hospital setting demonstrated that a closed-loop glycemic control system is expected to the achievement of TGC with no occurrence of hypoglycemia induced by IIT after surgery. Closed-loop glycemic control systems for IIT are now ready for prime time in the ICU.

Evaluating the Performance of a Novel Embedded Closed-loop System.

The objective was to assess the reliability of a novel automated closed-loop glucose control system developed within the AP@home consortium in adults with type 1 diabetes. Eight adults with type 1 diabetes on insulin pump therapy (3 men; ages 40.5 ± 14.3 years; HbA1c 8.2 ± 0.8%) participated in an open-label, single-center, single-arm, 12-hour overnight study performed at the clinical research facility. A standardized evening meal (80 g CHO) accompanied by prandial insulin boluses were given at 19:00 followed by an optional snack of 15 g at 22:00 without insulin bolus. Automated closed-loop glucose control was started at 19:00 and continued until 07:00 the next day. Basal insulin delivery (Accu-Chek Spirit, Roche) was automatically adjusted by Cambridge model predictive control algorithm, running on a purpose-built embedded device, based on real-time continuous glucose monitor readings (Dexcom G4 Platinum). Closed-loop system was operational as intended over 99% of the time. Overnight plasma glucose levels (22:00 to 07:00) were within the target range (3.9 to 8.0 mmol/l) for 75.4% (37.5, 92.9) of the time without any time spent in hypoglycemia (<3.9 mmol/l). Mean overnight glucose was 7.8 ± 1.3 mmol/l. For the entire 12-hour closed-loop period (19:00 until 07:00) plasma glucose levels were within the target range (3.9 to 10.0 mmol/l) for 84.4% (63.3, 100) of time. There were no adverse events noted during the trial. We observed a high degree of reliability of the automated closed-loop system. The time spent in target glucose level overnight was comparable to results of previously published studies. Further developments to miniaturize the system for home studies are warranted.

Artificial Pancreas: Model Predictive Control Design from Clinical Experience

The objective of this research is to develop a new artificial pancreas that takes into account the experience accumulated during more than 5000 h of closed-loop control in several clinical research centers. The main objective is to reduce the mean glucose value without exacerbating hypo phenomena. Controller design and in silico testing were performed on a new virtual population of the University of Virginia/Padova simulator. A new sensor model was developed based on the Comparison of Two Artificial Pancreas Systems for Closed-Loop Blood Glucose Control versus Open-Loop Control in Patients with Type 1 Diabetes trial AP@home data. The Kalman filter incorporated in the controller has been tuned using plasma and pump insulin as well as plasma and continuous glucose monitoring measures collected in clinical research centers. New constraints describing clinical knowledge not incorporated in the simulator but very critical in real patients (e.g., pump shutoff) have been introduced. The proposed model predictive control (MPC) is characterized by a low computational burden and memory requirements, and it is ready for an embedded implementation. The new MPC was tested with an intensive simulation study on the University of Virginia/Padova simulator equipped with a new virtual population. It was also used in some preliminary outpatient pilot trials. The obtained results are very promising in terms of mean glucose and number of patients in the critical zone of the control variability grid analysis. The proposed MPC improves on the performance of a previous controller already tested in several experiments in the AP@home and JDRF projects. This algorithm complemented with a safety supervision module is a significant step toward deploying artificial pancreases into outpatient environments for extended periods of time.

Technical Challenges and Clinical Outcomes of Using a Closed-Loop Glycemic Control System in the Hospital

According to large randomized trials, results suggest that maintaining normoglycemia postoperatively through tight glycemic control (TGC) and intensive insulin therapy (IIT) can improve surgical outcomes as well as reduce mortality and morbidity in critically ill patients. However, trials examining the effects of TGC have had conflicting results. Systematic reviews and meta-analyses have also led to differing conclusions. The main reason these clinical trials and meta-analyses show negative results for TGC is the high incidence of hypoglycemia induced by IIT. This could not be prevented because there is no reliable technique that can avoid this condition during IIT. The development of accurate, continuous blood glucose monitoring devices and closed-loop systems for computer-assisted blood glucose control in the intensive care unit (ICU) will probably help avoid hypoglycemia in these situations. The STG closed-loop glycemic control system was introduced to our department to be used and evaluated for strict serum glucose control with no hypoglycemic episodes during IIT in the surgical ICU, to reduce the workload of ICU nurses, and to decrease incidents related to the management of blood glucose levels according to manual conventional venous infusion insulin therapy. The goal of our team was to use the STG closed-loop glycemic control system for perioperative TGC in surgical patients to solve the complications of IIT and reduce risk of hypoglycemia. The challenge at our hospital demonstrated that the STG closed-loop glycemic control system can be expected to achieve TGC with no occurrence of hypoglycemia induced by IIT after surgery.

The use of an automated, portable glucose control system for overnight glucose control in adolescents and young adults with type 1 diabetes.

OBJECTIVEA key milestone in progress towards providing an efficacious and safe closed-loop artificial pancreas system for outpatient use is the development of fully automated, portable devices with fault detection capabilities to ensure patient safety. The ability to remotely monitor the operation of the closed-loop system would facilitate future physician-supervised home studies.RESEARCH DESIGN AND METHODSThis study was designed to investigate the efficacy and safety of a fully automated, portable, closed-loop system. The Medtronic Portable Glucose Control System (PGCS) consists of two subcutaneous glucose sensors, a control algorithm based on proportional-integral-derivative with insulin feedback operating from a BlackBerry Storm smartphone platform, Bluetooth radiofrequency translator, and an off-the-shelf Medtronic Paradigm Veo insulin pump. Participants with type 1 diabetes using insulin pump therapy underwent two consecutive nights of in-clinic, overnight, closed-loop control after a baseline open-loop assessment.RESULTSEight participants attended for 16 overnight studies. The PGCS maintained mean overnight plasma glucose levels of 6.4 ± 1.7 mmol/L (115 ± 31 mg/dL). The proportion of time with venous plasma glucose <3.9, between 3.9 and 8 (70 and 144 mg/dL), and >8 mmol/L was 7, 78, and 15%, respectively. The proportion of time the sensor glucose values were maintained between 3.9 and 8 mmol/L was greater for closed-loop than open-loop (84.5 vs. 46.7%; P < 0.0001), and time spent <3.3 mmol/L was also reduced (0.9 vs. 3%; P < 0.0001).CONCLUSIONSThese results suggest that the PGCS, an automated closed-loop device, is safe and effective in achieving overnight glucose control in patients with type 1 diabetes.